We use the SwinTrack [1]} to evaluate our pre-trained models. The data augmentations and the training settings Strictly follow SwinTrack [1]}.
We sample 131072 pairs per epoch and train the models for 300 epochs. We use the AdamW optimizer with a learning rate of \(5e\) -4 for the head, a learning rate of \(5e\) -5 for the backbone, and a weight decay of \(1e\) -4. We decrease the learning rate by a ratio of \(0.1\)  at the 210th epoch. We set the sizes of search images and templates as \(224\times 224\)  and \(112\times 112\) . The batch size is 160. The inference process is the same as the SwinTrack [1]}.
